š§µ From Suttonās Warning to Trustworthy Reasoning:
Why the next leap in AI isnāt bigger models itās verifiable reasoning.
Letās break down the TRUST Loop a framework that brings feedback, verification & learning into how LLMs think š
1/
Richard Sutton warned that LLMs are āa dead end.ā
They predict text but donāt learn from consequences.
They canāt test their own reasoning or improve through feedback.
Thatās the āreliability gapā AI that sounds smart but isnāt accountable.
2/
LLMs can write poetry and code fluentlyā¦
but still fail basic arithmetic or logic tasks.
When āalmost rightā isnāt good enough in finance,
safety, or science you need systems that can prove correctness, not just guess it.
3/
Enter the TRUST Loop Trusted Reasoning and Self-Testing.
Itās a closed-cycle framework that combines:
š¹ Planning
š¹ Deterministic execution
š¹ Independent verification
š¹ Self-correction
š¹ Transparent evidence reports
4/
Hereās how it works:
ā The LLM decomposes a query into checkable steps.
ā Each step runs through a deterministic or verified module (code, proof, or API).
ā Results are cross-checked by independent verifiers.
ā Any failure triggers automatic repair & re-run.
5/
The outcome:
ā
Zero unverified outputs
ā
Auditable reasoning traces
ā
Systems that learn from their own mistakes
Each computation becomes an interaction with truth, not just imitation of text.
6/
This moves us closer to Suttonās vision ā agents that learn from feedback, not just data.
The TRUST Loop doesnāt discard LLMs. It surrounds them with verifiable logic, feedback, and adaptation building the bridge from fluent to trustworthy.
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